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Accenture
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Custom Software Engineer

Custom Software Engineer

Accenture

2 - 5 years

5 - 9 Lacs

bengaluru

Posted:18 hours ago| Platform:

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Skills Required

software engineer s3 kubernetes python ai iac devops engineer machine learning docker gdpr lambda git automation iam compliance devops terraform agile prometheus aws infrastructure as code cloudformation architecture

Work Mode

Work from Office

Job Type

Full Time

Job Description

About The Role

Project Role :Custom Software Engineer

Project Role Description :Develop custom software solutions to design, code, and enhance components across systems or applications. Use modern frameworks and agile practices to deliver scalable, high-performing solutions tailored to specific business needs.
Must have skills :Machine Learning Operations

Good to have skills :Python (Programming Language), AWS Architecture, AWS AI Services
Minimum 5 year(s) of experience is required

Educational Qualification :15 years full time education
SummaryWe are seeking an experienced MLOps Engineer with strong expertise in building, automating, and maintaining end-to-end Machine Learning pipelines on AWS. The ideal candidate will have hands-on experience with AWS ML services, Infrastructure-as-Code, CI/CD for ML workflows, and scalable model deployment. This role requires strong collaboration, operational excellence, and the ability to support production-grade ML systems.________________________________________Roles & Responsibilities

Design and implement end-to-end ML pipelines—including data processing, model training, validation, deployment, and monitoring—using AWS services such as SageMaker, Lambda, Step Functions, ECS/ECR, and S3.

Develop Infrastructure-as-Code (IaC) using AWS CloudFormation, Terraform, or AWS CDK to automate deployments and resource provisioning.

Monitor and maintain model performance in production environments; build CI/CD workflows for ML models using CodePipeline, CodeBuild, and CodeDeploy.

Collaborate with data scientists, engineering teams, and DevOps to streamline model lifecycle management and ensure scalable ML operations.

Automate model retraining, rollback, and validation processes; establish robust testing and quality checks.

Manage containerization and orchestration using Docker, Kubernetes, EKS/ECS.

Document ML pipelines, operational procedures, and best practices to ensure long-term system maintainability.________________________________________Professional & Technical Skills

Hands-on experience with AWS AI/ML and compute services (SageMaker, Lambda, S3, IAM, Step Functions, CloudFormation, ECS/EKS).

Strong background in MLOps, including ML model lifecycle management, automation, monitoring, and production support.

Proficiency in CI/CD workflows and DevOps tooling (Git, CodePipeline, CodeBuild, CloudWatch, Prometheus).

Knowledge of security, compliance, and cloud governance best practices (e.g., HIPAA, GDPR).

Strong communication, documentation, and cross-functional collaboration skills.________________________________________
Additional Information

Experience with large-scale distributed systems is a plus.

Familiarity with feature stores, model registries, or domain-specific ML frameworks is an advantage.

Ability to work in fast-paced environments and support multiple ML initiatives concurrently.

Certifications in AWS (e.g., AWS Certified Machine Learning – Specialty, DevOps Engineer – Professional) are preferred but not mandatory.
Qualification 15 years full time education

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Custom Software Engineer